全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于谱聚类欠取样的不均衡数据SVM分类算法

, PP. 1761-1768

Keywords: 不均衡数据,SVM,算法,谱聚类,欠取样

Full-Text   Cite this paper   Add to My Lib

Abstract:

提出一种基于谱聚类欠取样的不均衡数据SVM分类算法。该算法首先在核空间中对多数类样本进行谱聚类,然后在每个聚类中根据聚类大小和该聚类与少数类样本间的距离,选择具有代表意义的信息点,最终实现训练样本间的数目均衡。实验中将该算法同其他不均衡数据预处理方法比较,结果表明该算法不仅能有效提高SVM算法对少数类的分类性能,而且总体分类性能及运行效率都有明显提高。

References

[1]  Haibo H,Edwardo A.Learning from Imbalanced Data[J].IEEE Transaction on Knowledge and Data Engineering,2009,21(9):1263-1284.
[2]  Liu X.Y.,Zhou Z.H.Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem[J]. IEEE Transactions on Knowledage and Data Engineering, 2006,18(1):63-77.
[3]  Van Hulsej J,Khoshgoftaar TM, Napolitano A. Experimental Perspectives on Learning from Imbalanceed Data[C] In Proceedings of the 24th International Conference on Machine Learning. New York:ACM. 2007:143-146.
[4]  Estabrooks A., Jo,T.. A Multiple Resampling Method for Learning from Imbalanced Data Sets[J].2004,20:18-36.
[5]  Han H.,Wang W.Y.,Mao B.H.. Borderline-SMOTE:A New Over-Sampling Method in Imbalanced Data Sets Learning[C].Proc.International Conference of Intelligent Computing,2005,878-887.
[6]  Akban I R, Kwek S, Japkow I.. Applying Support Vector Machines to Imbalanced Datasets[C] Proc of the 15th European Conference on Machines Learning. 2004:39-50.
[7]  陶新民,徐晶,童稚靖.不均衡数据下基于阴性免疫的过抽样算法,控制与决策,2010,25(6):867-873.
[8]  Tao Xinmin,Xu Jing,Tong Zhijing. Over-sampling algorithm based on negative immune in imbalanced data sets learning[J],Control and Decision, 2010,25(6):867-873.
[9]  Sun,Y.,Kamel M.S. and Wong A.K.C. Cost-Sensitive Boosting for Classification of Imbalanced Data[J].Pattern Recognition,2007,40(12):3358-3378.
[10]  曾志强,吴群,廖备水,高济.一种基于核SMOTE的非平衡数据集分类方法[J].电子学报,2009,39(11):2489-2495.
[11]  He,H,Bai Y. Garcia E.A. ADASYN:Adaptive Synthetic Sampling Approach for Imbalanced Learning[C].Proc international Conference of Neural Networks,2008:1322-1328.
[12]  BI Hua,L IAN G HongLi,WANG Jue. Resampling Methods and Machine Learning[J]. Chinese Journal of Computers, 2009, 32( 5) : 862-877.
[13]  王 玲, 薄列峰, 焦李成.密度敏感的半监督谱聚类[J]. 软件学报,2007,18(10):2412-2422.
[14]  Vapnik V N. The Nature of Statistical Learning Theory [M] .New York: Springer, 2000:138-167.
[15]  Liu X.Y.,Zhou Z.H. Exploratory Under-Sampling for Class-Imbalance Learing[J].IEEE Transactions on Systems,Man and Cybernetics.2009,39(2):539-550.
[16]  Weiss G.M. Mining with Rarity: A Unifying Framework[J].ACM SIGKDD explorations Newsletter, 2004, 6(1):7-19.
[17]  Bastista G.E.,Prati,R.C.,and Monard M.C. A study of the Behavior of Several Methods for Balancing Machine Learning Training Data[J]. ACM SIGKDD Exploration Newsletter, 2004,6(1):20-29.
[18]  ZENG Zhiqiang, WU Qun, LIAO Beishui, GAO Ji. A Classfication Method For Imbalance Data Set Based on Kernel SM0TE[J]. Acta Electronica Sinica,, 2009,39(11):2489-2495.
[19]  毕华,梁洪力,王珏.重采样方法与机器学习[J] . 计算机学报,2009, 32( 5) : 862-877.
[20]  Y.Liu,X.H. Yu.Combining integrated sampling with SVM ensembles for learning from imbalanced datasets[J].Information Processing & Management,2010,12:409-439.
[21]  WANG Ling, BO LieFeng, JIAO LiCheng. Density-Sensitive Semi-Supervised Spectral Clustering[J]. Journal of Software, 2007,18(10):2412-2422.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133